Abstract
This study aims to explore the scheduling and optimization of public transportation systems in the Internet of Things (IoT) environment. By establishing mathematical models, this research analyzes the current state of public transportation systems, proposes scheduling and optimization strategies based on mathematical models, and validates the effectiveness of these strategies through case studies. The research findings indicate that mathematical models have significant potential in improving public transportation efficiency, reducing costs, promoting environmental sustainability, and enhancing passenger experiences. Future research directions include real-time data integration, the application of machine learning, multi-modal transportation integration, sustainable practices, and the development of user-centric solutions.
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